The SANET Toolbox: New Methods for Network Spatial Analysis

Abstract

This paper describes new methods, called network spatial methods, for analyzing spatial phenomena that occur on a network or alongside a network (referred to as network spatial phenomena). First, the paper reviews network spatial phenomena discussed in the related literature. Second, the paper shows the uniform network transformation, which is used in the study of non‐uniform distributions on a network, such as the densities of traffic and population. Third, the paper outlines a class of network spatial methods, including nearest neighbor distance methods, K‐function methods, cell count methods, clumping methods, the Voronoi diagrams and spatial interpolation methods. Fourth, the paper shows three commonly used computational methods to facilitate network spatial analysis. Fifth, the paper describes the functions of a GIS‐based software package, called SANET, that perform network spatial methods. Sixth, the paper compares network spatial methods with the corresponding planar spatial methods by applying both methods to the same data set. This comparison clearly demonstrates how different conclusions can result. The conclusion summarizes the major findings.

Identifying Vehicular Crash High Risk Locations along Highways via Spatial Autocorrelation Indices and Kernel Density Estimation

“…The network-constrained Kernel Density Estimation is determined using the SANET V4.1 software [34]. Traditionally, network events are analyzed with spatial methods assuming Euclidean distance on a 2-D plane, however, this assumption does not hold in practice when analyzing network events, because Euclidean distances and their corresponding network shortest-path distances are significantly different.…”

Computational Streetscapes

“…Now, data science is being increasingly trained on fusion across big data. For example, network science and GIS are leading the way in providing structure across unstructured big data that streetscapes often cast in the course of their everyday dynamics [420][421][422]. Indeed, these types of approaches may be what the community needs moving forward in an era of computational social science that is beginning to fuse a wealth of qualitative and observational work [21] with near-ubiquitous sensing [423] and big data capabilities [424,425].…”

Predictive modeling for preventive Archaeology: overview and case study

“…KDEN could be calculated in a very different way and in different application fields (see for example [29], [30], [31], [32], [33], [34], [35], [36]), but it has not yet been applied in the archaeological field. In this paper the method proposed in SANET software has been used [37], [38], [39].…”